Run item
gemini · looker · question_index=3
Question
What are the best alternatives to Postgres for analytics?
Answer
PostgreSQL is a robust relational database system, but for highly demanding analytical workloads, several alternatives offer superior performance, scalability, and specialized features. These alternatives often fall into categories like cloud data warehouses and columnar databases, which are optimized for analytical processing.
Some of the best alternatives to PostgreSQL for analytics include:
* **Cloud Data Warehouses (e.g., Google BigQuery, Snowflake, Amazon Redshift)**: These platforms are designed for large-scale data analytics and offer significant advantages over traditional relational databases for OLAP (Online Analytical Processing) workloads.
* **Google BigQuery** is a fully managed, serverless data warehouse known for its exceptional performance, especially with petabyte-scale datasets. It's 100% elastic, allocating resources on demand for fast query execution. BigQuery's serverless nature also contributes to ease of use, scalability, and cost efficiency due to reduced operational overhead. It uses a columnar storage format optimized for data compression and query performance.
* **Snowflake** is another cloud-native data warehouse optimized for large-scale analytics and SQL queries. It separates compute and storage, allowing for elastic scaling and efficient handling of massive datasets. Snowflake is well-suited for various data types, including structured and semi-structured data.
* **Amazon Redshift** is a specialized data warehouse based on PostgreSQL, but architecturally optimized for analytics using a columnar storage model and massively parallel processing (MPP) across multiple nodes. This makes Redshift significantly better for large volumes of analytical data compared to row-oriented PostgreSQL.
* **Columnar Databases (e.g., ClickHouse, Apache Druid)**: These databases store data in columns rather than rows, which is highly efficient for analytical queries that often only need to access a subset of columns across many rows.
* **ClickHouse** is an open-source, column-oriented database specifically designed for fast analytical queries and real-time analytics. It excels at aggregating large volumes of data and can be significantly faster than PostgreSQL for analytical workloads on datasets starting around 1 million rows. ClickHouse prioritizes horizontal scaling and distributed query execution.
* **Apache Druid** is a distributed, column-oriented, real-time analytics data store often used for powering exploratory dashboards in multi-tenant environments. Druid is optimized for fast aggregate queries on petabyte-sized datasets and excels at real-time ingestion and low-latency analytics, especially for time-series data.
* **Distributed SQL Databases (e.g., Trino, CockroachDB, YugabyteDB, TiDB)**: These databases offer PostgreSQL compatibility while providing horizontal scalability and improved performance for analytical workloads.
* **Trino (formerly PrestoSQL)** is a distributed SQL query engine designed for high-performance data engineering and analytics on very large datasets. It acts as a high-performance SQL layer that can query various data sources, including PostgreSQL, making it suitable for federated analytics.
* **CockroachDB** and **YugabyteDB** were built to address PostgreSQL's horizontal scaling limitations, combining PostgreSQL's query layer with distributed storage architectures.
* **TiDB** also handles both transactional and analytical workloads without the need for separate data warehouses and scales horizontally.
While PostgreSQL can handle analytical workloads at smaller scales and offers advanced features like window functions and CTEs, its row-oriented storage and vertical scaling limitations make it less ideal for very large datasets, real-time analytics, and highly concurrent analytical queries. For these scenarios, the specialized architectures of cloud data warehouses and columnar databases often provide better performance, scalability, and cost-effectiveness.
Citations
No citations found.
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"text": "PostgreSQL is a robust relational database system, but for highly demanding analytical workloads, several alternatives offer superior performance, scalability, and specialized features. These alternatives often fall into categories like cloud data warehouses and columnar databases, which are optimized for analytical processing.\n\nSome of the best alternatives to PostgreSQL for analytics include:\n\n* **Cloud Data Warehouses (e.g., Google BigQuery, Snowflake, Amazon Redshift)**: These platforms are designed for large-scale data analytics and offer significant advantages over traditional relational databases for OLAP (Online Analytical Processing) workloads.\n * **Google BigQuery** is a fully managed, serverless data warehouse known for its exceptional performance, especially with petabyte-scale datasets. It\u0027s 100% elastic, allocating resources on demand for fast query execution. BigQuery\u0027s serverless nature also contributes to ease of use, scalability, and cost efficiency due to reduced operational overhead. It uses a columnar storage format optimized for data compression and query performance.\n * **Snowflake** is another cloud-native data warehouse optimized for large-scale analytics and SQL queries. It separates compute and storage, allowing for elastic scaling and efficient handling of massive datasets. Snowflake is well-suited for various data types, including structured and semi-structured data.\n * **Amazon Redshift** is a specialized data warehouse based on PostgreSQL, but architecturally optimized for analytics using a columnar storage model and massively parallel processing (MPP) across multiple nodes. This makes Redshift significantly better for large volumes of analytical data compared to row-oriented PostgreSQL.\n\n* **Columnar Databases (e.g., ClickHouse, Apache Druid)**: These databases store data in columns rather than rows, which is highly efficient for analytical queries that often only need to access a subset of columns across many rows.\n * **ClickHouse** is an open-source, column-oriented database specifically designed for fast analytical queries and real-time analytics. It excels at aggregating large volumes of data and can be significantly faster than PostgreSQL for analytical workloads on datasets starting around 1 million rows. ClickHouse prioritizes horizontal scaling and distributed query execution.\n * **Apache Druid** is a distributed, column-oriented, real-time analytics data store often used for powering exploratory dashboards in multi-tenant environments. Druid is optimized for fast aggregate queries on petabyte-sized datasets and excels at real-time ingestion and low-latency analytics, especially for time-series data.\n\n* **Distributed SQL Databases (e.g., Trino, CockroachDB, YugabyteDB, TiDB)**: These databases offer PostgreSQL compatibility while providing horizontal scalability and improved performance for analytical workloads.\n * **Trino (formerly PrestoSQL)** is a distributed SQL query engine designed for high-performance data engineering and analytics on very large datasets. It acts as a high-performance SQL layer that can query various data sources, including PostgreSQL, making it suitable for federated analytics.\n * **CockroachDB** and **YugabyteDB** were built to address PostgreSQL\u0027s horizontal scaling limitations, combining PostgreSQL\u0027s query layer with distributed storage architectures.\n * **TiDB** also handles both transactional and analytical workloads without the need for separate data warehouses and scales horizontally.\n\nWhile PostgreSQL can handle analytical workloads at smaller scales and offers advanced features like window functions and CTEs, its row-oriented storage and vertical scaling limitations make it less ideal for very large datasets, real-time analytics, and highly concurrent analytical queries. For these scenarios, the specialized architectures of cloud data warehouses and columnar databases often provide better performance, scalability, and cost-effectiveness.",
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Error json
null